Applications to Modelling and Predictive Control: Development and Validation of a Semi-physical 1D Model for Virtual Engine Strategy Optimization

建模和预测控制的应用：用于虚拟发动机策略优化的半物理一维模型的开发和验证

• 批准号: 2703814
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2703814
• 关键词: Applications; Modelling; Predictive; Control; Development

In machine learning, regression is often considered a black box methodology used commonly for identification of suitable functions from a hypothesis. Its primary aim is to estimate a predicted function which leads to minimal expected error on future data, and not necessarily to benefit the overall understanding of the derived output-input relationship governed by the physical interpretation of the system. Consequently, such modelling applications are often highly constrained, allowing training solely for thinly dispersed linear models. To this day, the task of forming intelligent algorithms capable of nonlinear interpretations through physical system observation has received very little consideration, it nevertheless forms the foundation of the field of system identification. Typically, approaches for nonlinear system identification include autoregressively modelling time evolution or utilising Volterra series multidimensional convolution integrals. This project will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system through supervised neural network-based machine learning algorithms for system optimization. The project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, enabling major efficiency and power improvements for future powertrains. It will enable the full utilization of Freevalve's potential and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.

在机器学习中，回归通常被认为是一种黑箱方法，通常用于从假设中识别合适的函数。它的主要目的是估计一个预测函数，使其对未来数据的预期误差最小，而不一定有利于对由系统的物理解释所控制的导出的输出-输入关系的总体理解。因此，这样的建模应用通常是高度受限的，只允许训练稀疏的线性模型。迄今为止，通过物理系统观察形成能够进行非线性解释的智能算法的任务很少得到考虑，然而，它构成了系统识别领域的基础。通常，非线性系统识别的方法包括自回归建模时间演化或利用Volterra系列多维卷积积分。该项目将解决传统的基于地图的控制器设计和校准实践在动力总成开发中的不足。它将为系统识别提供新颖的、未来的、非线性的物理因果关系预测建模和实验方法，通过基于监督神经网络的机器学习算法进行系统优化，得出一个实时的控制系统。该项目将与科尼赛克汽车公司（Koenigsegg Automotive AB）和Freevalve AB合作，开发新型无凸轮发动机技术Freevalve，为未来的动力系统带来重大的效率和动力提升。它将使Freevalve的潜力得到充分利用，并减少有害气体和颗粒物质的排放，使该技术处于市场领先地位，为大规模实施做好准备。